1. Field of the Invention
This invention relates generally to communication systems, and, more particularly, to wireless communication systems.
2. Description of the Related Art
Base stations in wireless communication systems provide wireless connectivity to users within the geographic area, or cell, associated with the base station. The wireless communication links between the base station and each of the users typically include one or more downlink (or forward) channels for transmitting information from the base station to the mobile unit and one or more uplink (or reverse) channels for transmitting information from the mobile unit to the base station. Multiple-input-multiple-output (MIMO) techniques may be employed when the base station and, optionally, the user terminals include multiple antennas. For example, a base station that includes multiple antennas can transmit multiple independent and distinct signals to multiple users concurrently and on the same frequency band. MIMO techniques are capable of increasing the spectral efficiency of the wireless communication system roughly in proportion to the number of antennas available at the base station. However, the base station also requires information about the state of the downlink channel(s) to each of the users to select users that have approximately orthogonal downlink channels for concurrent transmission. The channel feedback may be provided by the users on the reverse link, but this increases overhead associated with the MIMO transmissions, which reduces the spectral efficiency of the wireless communication system.
Random fluctuations in the channel states can create sets of downlink channels that are approximately orthogonal. Thus, if the number of users associated with a base station is large, these random fluctuations naturally tend to create groups of users that have approximately orthogonal downlink channels. Opportunistic MIMO schemes identify these groups of users so that the interference between the concurrent transmissions from the base station to the users in the selected group is within an acceptable tolerance level. For example, let nT denote the number of transmit antennas at the base station and let K indicate the number of users connected to the base station. Each user is equipped with nR receive antennas. The channel coefficients between each transmit antenna and each receive antenna at user k can be assembled into an nR×nT matrix Hk, k=1, . . . , K.
In a multi-user MIMO system that employs linear pre-coding matrices, the base station can transmit concurrently to as many as nT users, which can be chosen from the population of K users. The relationship between transmit and receive signals can be represented as:y=HGd+n where d is an nT-dimensional vector containing the transmit symbols, y is the nR-dimensional vector of received signals, n is an nR-dimensional noise vector, and G is an nT×nT pre-coding matrix. Note that some of the entries of d may be zero if the base station chooses to transmit to less than nT users (this is sometimes termed “rank adaptation”). The mobile units and the base station also store copies of a codebook consisting of L quantization matrices, Ci, i=1, . . . , L, which are used to quantize information for transmission. Altogether, the L quantization matrices amount to nT·L column vectors, where each column vector has nT entries. Each mobile unit quantizes its single user channel direction to the codeword of the codebook that maximizes a given criterion. At the mobile side, scalar or vector quantization can be used.
The base station can generate the pre-coding matrices based on its knowledge of the matrices Hk, k=1, . . . , K. However, this knowledge is typically incomplete because the transmitter at the base station is not able to determine the exact values of the channel matrices Hk. The base station must therefore rely on feedback from each mobile unit that reports an estimate the mobile unit's single-user channel matrix Hk. For example, when the base station implements an opportunistic scheme, each user periodically reports channel direction information that includes information indicating a preferred subset of the column vectors (or code words) in the codebook of L quantization matrices, Ci, i=1, . . . , L. The channel direction information is reported via the reverse link to the base station. The users also report a quality indicator corresponding to a hypothetical transmission associated with each preferred column. The base station can then generate precoding matrices using the channel direction information provided by the mobile units.
A general solution for the optimal vector quantizer that maximizes the mutual information in a MIMO multi-user transmission is not known. Love, et al (“Grassmannian beamforming for multiple-input multiple-output wireless systems,” IEEE Trans. Inf. Theory, vol. 49, no. 10, pp. 2735-2747, October 2003) have demonstrated that the problem of maximizing the throughput for a MIMO single-user system with limited feedback is equivalent to the problem of packing one dimensional subspace known as Grassmannian line packing. However, this approach has not been extended to the multi-user case and in particular to zero forcing (ZF) based scheme.
Santipath and Honig (“Asymptotic capacity of beamforming with limited feedback,” in IEEE Int. Symp. Info. Theory, Chicago, Ill., USA, July 2004, “Signature optimisation for CDMA with limited feedback,” IEEE Trans. Inf. Theory, vol. 51, no. 10, pp. 3475-3492, October 2005) describe random vector quantization (RVQ) techniques. In RVQ techniques, quantization codewords are independently chosen from an isotropic distribution on an M-dimensional unit sphere, where M is the number of transmit antennas. The RVQ approach provides an estimate of the lower bound of the performance of a quantization scheme because any reasonably well-designed codebook should perform at least as well as RVQ. When the number of feedback bits is small, the lower bound could be very loose because a RVQ codebook does not uniformly cover the M-dimensional space.
Philips (“System-level simulation results for channel vector quantization feedback for MU-MIMO,” 3GPP TGS RAN WG1, R1-063028, November 2006) proposes a Fourier codebook construction that provides good performance for line-of-sight channels or channels with a small angle-of-spread. This codebook is constructed by extracting the top M rows of a discrete Fourier transform (DFT) matrix of size P, where P is the codebook size. A codeword is then selected from the codebook using feedback information provided by the mobile unit in response to information transmitted by the base station. However, the quantization value indicated by a code word selected from the codebook is fixed once transmission to the mobile unit has been scheduled. The technique described by Philips does not permit the quantization value for a scheduled transmission by the mobile unit to be refined once it has been selected. Thus, quantization values used by mobile units that are scheduled to transmit multiple frames cannot be modified by exploiting the previous quantization vectors.